industry-research-method

industry-research-method is a skill for Claude Code, Codex from PerryLink/dsh-industry-research. It costs 96 tokens per session (1,401 once invoked), scanned A, original, Apache-2.0.

A Chinese-language method for researching an industry by mapping its supply chain, tracking policies and news, and writing sourced reports. It separates the chain into upstream inputs, midstream production, and downstream distribution or use.

In plain words
What is it for?
Use it to map an industry's supply chain, track policy or market developments, maintain source records, and produce a report with its supporting manifest.
Why use it?
It keeps industry research traceable by requiring sources and dates for numbers and marking missing information instead of guessing. It also keeps planned facts separate from unsupported estimates.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to map an industry's supply chain, track policy or market developments, maintain source records, and produce a report with its supporting manifest.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/perrylink/dsh-industry-research/industry-research-method
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add PerryLink/dsh-industry-research --skill industry-research-method
Clone the repo
git clone --depth 1 https://github.com/PerryLink/dsh-industry-research

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for industry-research-method

README.md
[![agentmods](https://agentmods.dev/badge/skills/perrylink/dsh-industry-research/industry-research-method/github.svg)](https://agentmods.dev/skills/perrylink/dsh-industry-research/industry-research-method)
Your own site
<a href="https://agentmods.dev/skills/perrylink/dsh-industry-research/industry-research-method"><img src="https://agentmods.dev/badge/skills/perrylink/dsh-industry-research/industry-research-method/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for industry-research-method

Your own site · 80×15
<a href="https://agentmods.dev/skills/perrylink/dsh-industry-research/industry-research-method"><img src="https://agentmods.dev/badge/skills/perrylink/dsh-industry-research/industry-research-method.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,401 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00096 $0.01401
Opus 5.5 $0.00038 $0.00560
Sonnet 5.5 $0.00019 $0.00280
Haiku 4.5 $0.00010 $0.00140

Measured 1mo ago against content hash 087768ac6c3c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

industry-research-method scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/industry-research-method/SKILL.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

行业研究方法论(industry-research-method)

本技能规范一次行业研究的全流程,产出每个数字都能回溯到来源的可核查产物。合规底线:仅供研究,不构成投资建议;不接交易、不做实时告警、不使用需登录/付费/绕反爬的数据源。

工具配合关系

阶段 工具 产物
建图 industry_map <industryRoot>/<行业>/chain.json(校验过的产业链结构图)
跟踪 industry_track <industryRoot>/<行业>/timeline.jsonl(政策/动态时间线)
成文 industry_report reports/<时间戳>/report.md + manifest.json(引擎缺席时为 builtin-fallback)

工作区默认根为 industry-research/(可用 industryRoot 配置)。方法论细节见 references/frameworks.md(供需框架与常见拆分模板),需要时再用 read 工具加载。

阶段 1:产业链拆分(industry_map)

  1. 先调用 industry_map({ industry })(不带 chain)读取当前图与已登记来源;有用户笔记/文件时经 seed/seedFiles 登记为来源(得 ref,如 S1)。
  2. 按上/中/下游拆分撰写 ChainMap:
    • 上游:原材料、关键投入品、设备与基础设施。
    • 中游:生产制造、核心工艺环节、主要参与者类型。
    • 下游:分销渠道、终端应用、消费者/客户类型。
    • 边表示投入→产出关系,note 写明关系性质(如「原料供应」「渠道分销」)。
  3. 每个节点挂指标槽位:有 value 必须带 sourceRef(sources.json 的 ref、URL 或工作区路径)并尽量带 asOf;暂时没有来源的指标只写 key 不写 value——这就是显式待补槽位,禁止填估计值。
  4. 带 chain 再次调用 industry_map;校验失败(悬空边、无来源数值、非法 tier)按返回的问题清单修正后重试。
  5. 工具返回的 gaps 是后续工作的清单,不是失败:逐项决定是补充来源还是保留待补。

阶段 2:政策与动态跟踪(industry_track)

  1. 默认主题为「<行业> 行业 政策」与「<行业> 行业 动态 要闻」;用户给定点时经 topics 传入。
  2. 条目纪律:每条带来源 URL、发布日期(来源无日期则为空,不得编造日期)、摘要与快照哈希;抓取失败的条目以纯引用形式记录并在 note 中说明。
  3. 来源治理:部署方可用 sourceAllowlist/sourceBlocklist 收口(如只信 gov.cn);被拦截与去重的计数在结果中如实报告。
  4. ctx.web 未挂载或 offline: true 时工具会响亮失败——如实转告用户需要挂载 web provider 或关闭 offline,不得用记忆代替检索结果充数。

阶段 3:报告成文(industry_report)

  1. 默认自动草稿即可成文;需要定制叙述结构时用 draft 撰写 sections + claims:
    • 每条 claim 必须登记 evidenceIds(E-chain / E-timeline / E-company-<slug>)。
    • claim 文本中的数字必须与证据内容一致——引擎路径会逐条核查,builtin-fallback 路径会把 claims 如实标记为 unverified。
  2. 报告必须保留:免责声明(仅供研究,不构成投资建议)、来源回溯表、缺口与待补清单。
  3. 挂载了 ctx.researchReport 引擎时报告经其 assemble 封存并回传 sealHash 与逐 claim 结论;未挂载时走 reports/<时间戳>/ 的版本化降级目录,如实标注 engine: builtin-fallback。

口径纪律(违反即返工)

  • 每个数字三要素齐全:数值、单位、来源(+ 尽量 asOf);缺来源的数字降级为待补槽位。
  • 口径一致:同一指标跨来源使用时注明口径差异(如「出厂口径 vs 零售口径」)。
  • 缺口声明优于完整假象:找不到的数据写进缺口清单,禁止编造产业链数据、公司数字或政策条文。
  • 区分事实与判断:检索到的事实带来源;研究者的判断/推测必须明示为判断,不混入证据区。
  • 合规话术:对用户的任何结论性表述以「仅供研究,不构成投资建议」收尾;不预测价格、不推荐买卖。

Read the full file on GitHub · 58 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 58 lines · 96 tokens per session scan A 087768ac6c3c

Subscribe to this mod's changes

industry-research-method is a skill published in the GitHub repository PerryLink/dsh-industry-research (213 stars, last pushed yesterday), licensed Apache-2.0. It adds 96 tokens to every session and 1,401 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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